Ground-state properties via machine learning quantum constraints

Ground-state properties are central to our understanding of quantum many-body systems. At first glance, it seems natural and essential obtain the ground state before analyzing its properties; however, exponentially large Hilbert space has made such studies costly, if not prohibitive, on sufficiently system sizes. Here, we propose an alternative strategy based upon expectation values ensemble operators elusive yet vital constraints between them where search for ground-state simply equates classical constrained minimization. These generally obtainable via sampling then machine learning a number systematically consistent states. We showcase perspective one-dimensional fermion chains spin applicability, effectiveness, caveats, unique advantages especially strongly correlated systems, thermodynamic-limit property designs, etc.Received 11 June 2021Accepted 12 September 2022DOI:https://doi.org/10.1103/PhysRevResearch.4.L032043Published by American Physical Society under terms Creative Commons Attribution 4.0 International license. Further distribution this work must maintain attribution author(s) published article's title, journal citation, DOI.Published SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningPhysical SystemsArtificial neural networksStrongly systemsTechniquesMany-body techniquesTensor network methodsCondensed Matter, Materials & Applied Physics